Files
Jungfraujoch/image_analysis/bragg_integration/BackgroundAperture.cpp
T
leonarski_f 711f902aa7 One gain mechanism: frames analysed in their own units, the gain applied where a photon count is assumed
The gain of an input in detector units (SMV, marCCD, XTC, the XFEL formats) is measured once, before
anything reads the frames (RugnuxMeasureGain: photon-peak spacing and background variance per unit
mean), or given with --gain, and handed to the integration and the spot finding by one function
(ApplyRugnuxPixelGain). The rotation pre-scan no longer measures it, and the stills reader no longer
converts frames of a detector whose photons are not resolved: SACLA MPCCD, Rayonix and CCD frames are
integrated in ADU at the gain, rotation and stills alike.

Where a photon count was assumed, it is now taken at the gain (bit-identical at gain 1):
- integration: the background clip's one-photon floor, the sigma floor (one photon), the profile fit's
  background floor (0.01 photon), and a still's overload kept as a lower bound, which was left in ADU
  while every other reflection left the integrator in photons - the cause of the earlier loss of the
  engine route on clipped CCD stills (merged low resolution inflated by the gain, the rocking-width
  fit's bandwidth collapsing); CPU and CUDA, Metal through the shared CPU code;
- the adaptive spot threshold (Poisson arm and its one-photon floor), CPU and GPU;
- the pre-scan spot-width measurement (strong-spot SNR, the ring clip) behind the stills bandwidth and
  the rotation signal radius;
- the hot/dead pixel tests, CPU and GPU; the empty-aperture ring clip.
BackgroundPixelGain is gone: one pixel_gain.

A detector whose background resolves single photons (photon peaks: JUNGFRAU in keV, AGIPD) is a photon
counter and its frames are still counted in whole photons as read: analysed in keV at the gain instead,
a photon-counting still set lost 0.008 R-free (fewer spots, a noisier beam-centre trial) - the rounding
is the detector's own discrimination. --gain sets the value, the background still says which kind.

Validation, against rc178: photon-counting and g<=1 rotation p.hkl md5-identical (lyso, thau, cytc,
myob, insu, a small-molecule set, 5reo, 6cee, 6i3j, 2xfw, 6s1u, 8v4j); CCD g>1 at or within noise of rc178 (5uth
R_meas 0.113 -> 0.108). Stills R-free (0-cycle REFMAC): MPCCD sets 0.184 -> 0.185 and 0.199 -> 0.196,
Rayonix identical, photon-resolving sets back on their rc178 numbers.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015CeEGgpKdfkCTJb3YNMJcu
(cherry picked from commit da8dbfc8147354b63e375f2d9521dffb80938b6d)
2026-10-23 17:56:51 +02:00

182 lines
7.3 KiB
C++

// SPDX-FileCopyrightText: 2026 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#include "BackgroundAperture.h"
#include <algorithm>
#include <cmath>
#include "../../common/JFJochMath.h"
std::vector<float> background_aperture::CandidateR3(float r2, float max_r3) {
std::vector<float> out;
for (float r3 = r2 + 2.0f; r3 <= max_r3 + 1e-3f; r3 += 1.0f)
out.push_back(r3);
return out;
}
void background_aperture::MeasureFrame(ImagePreprocessorBuffer &image, int width, int height,
const std::vector<DiffractionSpot> &spots, float r1, float r2,
const std::vector<float> &r3, float clip_nsigma, float gain,
const RingBackground &rings, const DiffractionGeometry &geometry,
const DetectorModules &modules, Tally &tally) {
if (r3.empty())
return;
tally.sum_d2.resize(r3.size(), 0.0);
tally.sum_d2_radial.resize(r3.size(), 0.0);
tally.sum_dd2.resize(r3.size(), 0.0);
const float r_max = r3.back();
const int half = static_cast<int>(std::ceil(r_max));
const int side = 2 * half + 1;
// A grid of centres, at least one aperture apart, kept clear of every found spot.
const double area = static_cast<double>(width) * height;
const int step = std::max(side, static_cast<int>(std::ceil(std::sqrt(area / MAX_APERTURES_PER_FRAME))));
const float clear = r_max + SPOT_CLEARANCE_PX;
std::vector<int32_t> cx, cy;
for (int y = half; y + half < height; y += step)
for (int x = half; x + half < width; x += step) {
bool near_spot = false;
for (const auto &s : spots) {
const Coord c = s.RawCoord();
if ((c.x - x) * (c.x - x) + (c.y - y) * (c.y - y) < clear * clear) {
near_spot = true;
break;
}
}
if (!near_spot) {
cx.push_back(x);
cy.push_back(y);
}
}
if (cx.empty())
return;
const int32_t *tiles = image.GatherTiles(cx, cy, width, half);
struct Pixel {
int32_t v;
float d2; // squared distance from the aperture centre
double b; // the ring background at this pixel's resolution
};
std::vector<Pixel> disk, ring;
for (size_t a = 0; a < cx.size(); ++a) {
const int32_t *t = tiles + a * static_cast<size_t>(side) * side;
const int64_t panel = modules.ModuleOfImagePixel(cx[a], cy[a]);
bool readable = true;
disk.clear();
ring.clear();
for (int dy = -half; dy <= half && readable; ++dy)
for (int dx = -half; dx <= half; ++dx) {
const float d2 = static_cast<float>(dx * dx + dy * dy);
if (d2 >= r_max * r_max || (d2 >= r1 * r1 && d2 < r2 * r2))
continue;
const int32_t v = t[(dy + half) * side + dx + half];
if (v == INT32_MIN || v == INT32_MAX) {
readable = false;
break;
}
double b = 0.0;
if (!rings.empty()) {
const auto [px, py] = modules.ImageToPlane(panel, cx[a] + dx, cy[a] + dy);
b = rings.At(2.0 * PI / geometry.PxlToRes(static_cast<float>(px), static_cast<float>(py)));
}
(d2 < r1 * r1 ? disk : ring).push_back({v, d2, b});
}
if (!readable || disk.empty())
continue;
double disk_v = 0.0, disk_b = 0.0;
for (const auto &p : disk) {
disk_v += p.v;
disk_b += p.b;
}
disk_v /= static_cast<double>(disk.size());
disk_b /= static_cast<double>(disk.size());
// Every candidate ring as the integrator reads it: the mean, then one high-side clip pass. The
// radial correction is taken over the ring's geometry, as the integrator's kernel is.
std::vector<double> d_plain(r3.size()), d_radial(r3.size());
bool measured = true;
for (size_t k = 0; k < r3.size() && measured; ++k) {
const float r3_sq = r3[k] * r3[k];
double sum = 0.0, sum_b = 0.0;
int n = 0;
for (const auto &p : ring)
if (p.d2 < r3_sq) {
sum += p.v;
sum_b += p.b;
++n;
}
if (n == 0) {
measured = false;
break;
}
const double mean = sum / n;
const double thr = mean + clip_nsigma * std::sqrt(gain * std::max(mean, static_cast<double>(gain)));
double kept = 0.0;
int n_kept = 0;
for (const auto &p : ring)
if (p.d2 < r3_sq && (clip_nsigma <= 0.0f || p.v <= thr)) {
kept += p.v;
++n_kept;
}
measured = n_kept > 0;
if (measured) {
d_plain[k] = disk_v - kept / n_kept;
d_radial[k] = d_plain[k] - (disk_b - sum_b / n);
}
}
if (!measured)
continue;
for (size_t k = 0; k < r3.size(); ++k) {
tally.sum_d2[k] += d_plain[k] * d_plain[k];
if (!rings.empty()) {
tally.sum_d2_radial[k] += d_radial[k] * d_radial[k];
const double dd = d_radial[k] * d_radial[k] - d_plain[k] * d_plain[k];
tally.sum_dd2[k] += dd * dd;
}
}
++tally.apertures;
if (!rings.empty())
++tally.apertures_radial;
}
}
void background_aperture::Tally::Add(const Tally &other) {
sum_d2.resize(std::max(sum_d2.size(), other.sum_d2.size()), 0.0);
sum_d2_radial.resize(std::max(sum_d2_radial.size(), other.sum_d2_radial.size()), 0.0);
for (size_t k = 0; k < other.sum_d2.size(); ++k)
sum_d2[k] += other.sum_d2[k];
for (size_t k = 0; k < other.sum_d2_radial.size(); ++k)
sum_d2_radial[k] += other.sum_d2_radial[k];
sum_dd2.resize(std::max(sum_dd2.size(), other.sum_dd2.size()), 0.0);
for (size_t k = 0; k < other.sum_dd2.size(); ++k)
sum_dd2[k] += other.sum_dd2[k];
apertures += other.apertures;
apertures_radial += other.apertures_radial;
}
std::vector<double> background_aperture::Tally::MeanSquare(bool radial) const {
const size_t n = radial ? apertures_radial : apertures;
std::vector<double> out;
if (n == 0)
return out;
for (const double v : radial ? sum_d2_radial : sum_d2)
out.push_back(v / static_cast<double>(n));
return out;
}
double background_aperture::Tally::RadialZ(size_t k) const {
if (apertures_radial < 2 || apertures_radial != apertures || k >= sum_dd2.size())
return 0.0;
const double n = static_cast<double>(apertures_radial);
const double mean = (sum_d2_radial[k] - sum_d2[k]) / n;
const double var = std::max(sum_dd2[k] / n - mean * mean, 0.0);
return var > 0.0 ? -mean / std::sqrt(var / n) : 0.0;
}
int background_aperture::Best(const std::vector<double> &mean_square) {
if (mean_square.empty())
return -1;
return static_cast<int>(std::min_element(mean_square.begin(), mean_square.end()) - mean_square.begin());
}